arXiv Machine Learning
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
4d ago
Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
arXiv:2506.20651v1 Announce Type: cross Abstract: Recent work has shown that gradient updates in federated learning (FL) can unintentionally reveal sensitive information about a client's local data....
arXiv Machine Learning
Jun 16
Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning
arXiv:2606. 17035v1 Announce Type: new Abstract: Prior research suggests that differential privacy (DP) inherently enhances the robustness of federated learning (FL) against backdoor attacks.
arXiv AI
Jun 2
Safe-FedLLM: Delving into the Safety of Federated Large Language Models
arXiv:2601. 07177v5 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs).
arXiv AI
Jul 20
Poison to Detect: Detection of Targeted Overfitting in Federated Learning
arXiv:2509. 11974v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET).
arXiv Machine Learning
Jul 21
Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering
arXiv:2310. 16152v5 Announce Type: replace-cross Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.
arXiv Machine Learning
Aug 24
BackDFL: A Unified Benchmark For Backdoor Attacks and Defenses In Decentralized Federated Learning
arXiv:2608.21137v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model...